Spectral analysis for the early detection of anthracnose in fruits of Sugar Mango (Mangifera indica)

Abstract

The use of spectroscopy in fruits provides spectral information that can be used to estimate chemical-physiological variables or to determine the phytopathological state of the fruit. Mango is a fruit prone to develop the anthracnose pathogen during its harvest, affecting its commercialization. There are different studies of mango that evaluate the development of anthracnose, however, no work in the previous literature has presented a method to estimate early the state of development of anthracnose. In this work, a spectroradiometer was used to evaluate the evolution of anthracnose in mango fruits. Three stages of development in the mango were analyzed (healthy, asymptomatic and diseased) and the performance was evaluated with random forest (RF) and support vector machines (SVM). The principal component analysis (PCA) and linear discriminant analysis (LDA) were used to reduce the dimensionality and identify the most significant bands of the spectrum used, with the help of a Gaussian filter. A total of 61 significant bands with PCA and 29 significant bands with LDA were found. The best evaluation performance was obtained with LDA reaching an accuracy of 91–100% in the three classes. The bands 399, 514, 726, 822, 912 and 1061 nm of the set of 29 bands of LDA are highlighted to identify asymptomatic fruits. This non-destructive method to identify the development of anthracnose at an early stage could benefit the farmer by helping to improve the commercialization of mango. In general, early detection of anthracnose, which is not visible, reached an average accuracy in the 29 bands identified with 91% LDA.